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Abstract: Tool wear monitoring is important in machining industries for controlling the quality of machined parts that helps to improve the productivity. To date, many monitoring system methods are developed by utilizing various signals, and cutting force is one of the signals in machining process that has been widely used for tool wear monitoring. This paper presents the application of I-kaz based method to analyze the cutting force signal for monitoring the status of tool wear in turning process. Experiments were carried out by turning hardened carbon steel, and cutting force signals were measured by two channels of strain gauges that were mounted on the surface of tool holder. In this study, I-kaz 2D is one of the derivatives of I-kazTM method that has been utlized to integrate two components of cutting force signals. It differs from the I-kaz method, whereby the signal does not need to be decomposed to three different frequency ranges. The analysis of results using I-kaz 2D and I-kazTM methods, revealed that both methods can be used to determine tool wear progression during turning process and feed force is very significant due to flank wear. [Copyright &y& Elsevier]

Abstract: Tool wear is a detrimental factor that affects the quality and tolerance of machined parts. Having an accurate prediction of tool wear is important for machining industries to maintain the machined surface quality and can consequently reduce inspection costs and increase productivity. Online and real-time tool wear prediction is possible due to developments in sensor technology. Recently, various sensors and methods have been proposed for the development of tool wear monitoring systems. In this study, an online tool wear monitoring system was proposed using a strain gauge-type sensor due to its simplicity and low cost. A model, based on the adaptive network-based fuzzy inference system (ANFIS), and a new statistical signal analysis method, the I-kaz method, were used to predict tool wear during a turning process. In order to develop the ANFIS model, the cutting speed, depth of cut, feed rate and I-kaz coefficient from the signals of each turning process were taken as inputs, and the flank wear value for the cutting edge was an output of the model. It was found that the prediction usually accurate if the correlation of coefficients and the average errors were in the range of 0.989–0.995 and 2.30–5.08% respectively for the developed model. The proposed model is efficient and low-cost which can be used in the machining industry for online prediction of the cutting tool wear progression, but the accuracy of the model depends upon the training and testing data. [Copyright &y& Elsevier]